Based on multi-period ground gravity and geomagnetic observation data from 2016 to 2021 in western Yunnan, this study investigates the 2021 Yangbi M6.4 earthquake using a joint analysis method of gravity and magnetic fields. It analyzes the coupling characteristics of the gravity field and geomagnetic field during the seismogenic process and proposes a new short-term and imminent earthquake prediction method. The synchronously observed gravity and total magnetic intensity variation data were normalized, and the kernel density distribution and correlation coefficient changes of both within the same region were analyzed to study the epicenter location and the imminence of the earthquake occurrence. The results show that: 1) The gravity field changes and total magnetic intensity changes in the epicentral area are coupled, both exhibiting minor variations at the epicenter but significant changes in the surrounding areas. As the time of the earthquake approaches, the trends of both changes become consistent, while they rapidly diverge after the earthquake. 2) For the Yangbi earthquake, within a 1°×1° grid, the region with the largest overlap area of the annual variation kernel density curves of the gravity and magnetic fields is located between 25°-26°N and 99°-100°E, which is highly consistent with the actual epicenter of the Yangbi earthquake. 3) The joint analysis method also demonstrates practical applicability for earthquakes below magnitude 6 in the study area.

Based on the gravity observation data from the Hubei gravity network in 2019, we study the impact of datum points selection on the adjustment accuracy of gravity network from three aspects: the spatial distribution of datum points, the number of datum points, and the maximum gravity segment difference of selected datum points using the classical adjustment method, and obtain the optimal scheme for datum points selection in Hubei gravity network. Additionally, we conduct a forward simulation using priori mean error to evaluate the adjustment effects controlled by different datum points, and explore the optimal selection schemes for single-datum, dual-datum, triple-datum, and quadruple-datum configurations. The results show that: 1) The selection of datum points should prioritize their spatial distribution to maximize the control effect of each datum point. The number of datum points should be considered next, after a certain number is reached, the adjustment accuracy of gravity network will tend to saturate. Finally, the maximum gravity segment difference of selected datum points should be considered, with preference given to datum points with larger segment difference. 2) For the Hubei gravity network with fixed gravity datums, the average adjustment accuracy improvement rates from optimal single-datum point to optimal quintuple-datum points are 16.81%, 8.31%, 4.84%, and 2.98%, respectively. After statistical and simulation analysis, it is suggested that Hubei gravity network use four datum points of Wanzhou, Zigui, Xiangyang, and Wuhan. 3) Using priori mean error to simulate the selection of optimal datum points, the optimal single-datum point is Jingmen, the optimal dual-datum points are Fengjie and Wuhan, the optimal triple-datum points are Wanzhou, Buzhuanghe, and Wuhan, and the optimal quadruple-datum points are Wanzhou, Shiyan, Buzhuanghe, and Wuhan. Compared with the optimal datum points obtained from the actual data analysis, the simulated optimal datum points have a more rational spatial distribution and a slight improvement in adjustment accuracy. The priori mean error method used in this article to simulate the optimal datum points of gravity network has certain reference value for datum points optimization of the existing national network, regional network, or the planning gravity measurements.

To investigate the correlation between precipitable water vapor (PWV) and aerosol optical depth (AOD), a novel AOD estimation model was developed based on PWV. The model incorporates not only a linear relationship between PWV and AOD but also accounts for the periodic characteristics observed in the residuals of the linear fit. Utilizing high-precision PWV and AOD data from 20 stations in Yunnan from 2016 to 2019, the model was constructed and validated. The results reveal a strong lagged correlation between PWV and AOD in the region, with an average correlation coefficient of 0.82. The proposed model achieved excellent performance in AOD estimation, both in internal and external validations, yielding a mean standard deviation (STD) of 0.11 mm and a root mean square error (RMSE) of 0.12 mm. This model effectively paves the way for estimating high-precision AOD from PWV, thereby opening up new avenues for atmospheric environmental monitoring and research using GNSS remote sensing.

An empirical atmospheric weighted average temperature (T_m) model for Qinghai was established based on data from 7 radiosonde stations in Qinghai from 2019 to 2022. This model was then used to retrieve precipitable water vapor (PWV) from 42 GNSS stations in the province for analyzing the spatiotemporal characteristics of water vapor during multiple heavy rainfall processes in August 2022. The results show that: 1) The newly established T_m model has a mean bias of 0.4 K and a root mean square error (RMSE) of 3.41 K, indicating its accuracy is significantly superior to the Bevis model and China-regional models, while being comparable to that of the single-station models. 2) The GNSS PWV retrieved based on the localized T_m model highly agrees with radiosonde results (correlation coefficient of 0.98). 3) Before heavy rainfall occurs, PWV exhibits a rapid increase sustained for several hours, and its 3-hour variation (PWV^*) remains positive, whereas before weak rainfall, PWV shows fluctuating changes. 4) Short-term heavy rainfall is likely to occur when the regional PWV value exceeds 34 mm and PWV^* is positive; conversely, rainfall intensity is lower.

To investigate the impact of receiver IFCB on GLONASS undifferenced precise time transfer, different IFCB modeling schemes were proposed and their PPP time transfer performance was evaluated, namely the IFCB0 scheme ignoring IFCB, the IFCB1 scheme modeling IFCB as a linear function of frequency numbers, and the IFCB2 scheme modeling IFCB as a quadratic polynomial. The results indicate that IFCB modeling is of great significance for improving the accuracy of PPP time transfer. Compared to the IFCB0 scheme, the IFCB modeling approach demonstrates the most notable improvement in long-term frequency stability for time transfer. The IFCB modeling significantly enhances time transfer stability for hybrid and heterogeneous receiver/antenna types, while its improvement for homogeneous equipment is limited. Additionally, compatibility tests with different satellite precision products show that the IFCB2 scheme aligns best with the precision products from the European Orbit Determination Centre, whereas the IFCB1 scheme is more compatible with the precision products provided by Wuhan University.

Given the issue that multi-system satellite clock bias estimation is susceptible to interference from various noise types, an estimation model that fully considers the characteristics of satellite clock noise components is proposed. This model employs two strategies, namely sliding window and overall modeling, and utilizes the Hadamard variance to solve for noise parameters. Based on this, clock bias estimation and accuracy assessment are conducted. In the experimental section, multiple representative satellites from the four major satellite navigation systems—GPS, BDS-3, Galileo, and GLONASS are selected as research subjects. A systematic comparative analysis is performed on the performance differences of the two modeling strategies across different systems and satellites. The experimental results reveal that the BDS-3 exhibits significant inter-satellite variability. Specifically, most satellites in MEO demonstrate more pronounced time-varying noise characteristics, making them more suitable for the sliding modeling strategy. Conversely, for some satellites equipped with MEO rubidium clocks and stable IGSO satellites, the overall modeling strategy offers greater advantages. In the case of the Galileo, the global modeling strategy performs better. For the GPS and GLONASS, after adopting the sliding window strategy, the positioning accuracy in E, N, U directions improves by 10.34%, 17.07%, and 4.30%, respectively, and by 6.27%, 10.34%, and 3.76%, respectively, compared to the overall modeling strategy.

Accurate estimation of phase biases is an essential prerequisite for achieving PPP ambiguity resolution (AR). We focus on the estimation methods of observable-specific signal bias (OSB) and multi-frequency multi-GNSS PPP-AR technique, and conduct experimental analysis on the combined observations from GPS, Galileo and BDS at 30 MGEX stations during March 2024. The results show that the phase and pseudorange OSB have good stability, the STD of phase OSB in different systems is less than 0.05 ns, and the consistency of different types of inter-frequency phase and pseudorange OSB of each system is good. The wide-lane/narrow-lane ambiguity resolution rates reach 98.5%/96.7% for GPS, 99.3%/98.8% for Galileo, and 95.4%/94.9% for BDS. Compared with float solutions, the convergence times of triple-frequency PPP fixed solutions are reduced by 28.37% in kinematic scenario, shortened by 31.61% in static scenario. Under static condition, average single-day solution positioning accuracy in E, N and U directions improved by 24.91%(0.71 cm), 31.22%(0.50 cm), and 18.77%(1.10 cm), respectively, demonstrating significant enhancement in positioning performance.

Currently, the accuracy of medium- and long-term predictions of Earth rotation parameters (ERP) (prediction duration exceeding 30 days) can only reach the milliarcsecond level. This level of accuracy is significantly lower than the microarcsecond level required by astrometry and the millimeter level needed by space geodesy. This paper systematically evaluated the performance of four deep learning models (BP, RNN, LSTM, and Transformer) in short-, medium-, and long-term ERP forecasts, with a focus on long-term predictions. The experimental results show that in 180-day predictions, the precision of polar motion forecasts by the Transformer model is 43.8% higher than that of traditional methods, and the prediction error of length of day (LOD) is reduced to 0.27 ms. In UT1-TAI predictions, the BP model achieved the best accuracy with an RMSE of 7.83 ms. This study can provide reference for solving the medium- and long-term ERP forecasting challenges in the autonomous operation of the Beidou system.

Conventional SBAS-InSAR methods are limited by their inability to capture the full dimensionality of deformation, making it difficult to effectively represent three-dimensional (3D) surface displacements in mining areas. To address this issue, this study proposes a 3D deformation inversion method that integrates ascending and descending SAR data by combining SBAS-InSAR with Offset-Tracking techniques. Using the Liuhuanggou coal mine as a case study, experiments were conducted to derive the 3D surface deformation field. The inversion results were systematically analyzed in terms of subsidence magnitude, affected area, and spatial distribution. Furthermore, leveling data were employed to compare the accuracy of the proposed 3D deformation inversion method with conventional ascending and descending SBAS-InSAR 2D deformation results. This comparison aimed to evaluate the advantages of the 3D decomposition approach over existing 2D methods. The findings indicate that the 3D inversion method aligns more closely with actual leveling data and achieves higher inversion accuracy than the 2D approach. The proposed method enhances the monitoring precision of SBAS-InSAR to a certain extent and expands its measurement dimensionality, thereby providing a novel and effective technical reference for surface deformation monitoring in mining areas.

Existing landslide research is mostly limited to single deformation data, ignoring the influence of multidimensional environmental characteristics factors on landslide deformation, such as temperature, dew point temperature, precipitation, and spatiotemporal correlation. To address this issue, we propose a multivariate Transformer prediction model based on time series InSAR technology. The model integrates multidimensional environmental features and spatiotemporal correlation data, and uses wavelet transform to decompose deformation sequences into periodic and trend components. Different Transformer structures are constructed to predict each component. The results show that the prediction accuracy of this model is significantly better than that of traditional methods, with RMSE of 0.72 mm and R² of 0.96. This research provides a new approach for landslide deformation prediction and is of great significance for improving early warning capabilities.